Researchers announced the breakthrough discovery of a new type of antibiotic compound that is capable of killing many types of harmful bacteria, including deadly antibiotic-resistant strains, and published their findings in Cell on February 20. What makes this remarkable is that the researchers, from the Massachusetts Institute of Technology (MIT), Harvard, and McMaster University, used machine learning (a form of artificial intelligence) to discover the new antibiotic--an achievement that heralds the disruption of traditional research and drug development processes deployed by pharmaceutical industry behemoths. Antibiotic resistance is a global threat that is exacerbated by the overuse of antibiotics in livestock, the proliferation of antimicrobials in consumer products, and over-prescription in health care. Though estimating the future impact is challenging, one report predicted that by 2050, 10 million deaths per year could result from antimicrobial-resistant (AMR) infections. Combating the problem of antimicrobial resistance requires bringing novel compounds to market.

Using a machine-learning algorithm, MIT researchers have identified a powerful new antibiotic compound. In laboratory tests, the drug killed many of the world's most problematic disease-causing bacteria, including some strains that are resistant to all known antibiotics. It also cleared infections in two different mouse models. The computer model, which can screen more than a hundred million chemical compounds in a matter of days, is designed to pick out potential antibiotics that kill bacteria using different mechanisms than those of existing drugs. "We wanted to develop a platform that would allow us to harness the power of artificial intelligence to usher in a new age of antibiotic drug discovery," says James Collins, the Termeer Professor of Medical Engineering and Science in MIT's Institute for Medical Engineering and Science (IMES) and Department of Biological Engineering.

THERE ARE MANY REASONS that promising drugs wash out during pharmaceutical development, and one of them is cytochrome P450. A set of enzymes mostly produced in the liver, CYP450, as it is commonly called, is involved in breaking down chemicals and preventing them from building up to dangerous levels in the bloodstream. Many experimental drugs, it turns out, inhibit the production of CYP450--a vexing side effect that can render such a drug toxic in humans. Drug companies have long relied on conventional tools to try to predict whether a drug candidate will inhibit CYP450 in patients, such as by conducting chemical analyses in test tubes, looking at CYP450 interactions with better-understood drugs that have chemical similarities, and running tests on mice. But their predictions are wrong about a third of the time.

Immune systems across the globe have been working overtime this winter as a devastating flu season has taken hold. More than 180,000 Americans have been hospitalized and 10,000 more have died in recent months, according to the CDC, while the coronavirus (now officially designated COVID-19) has spread across the globe at an alarming rate. Fears of a growing worldwide flu outbreak have even prompted the precautionary cancelling of MWC 2020 -- barely a week before it was slated to open in Barcelona. But in the near future, AI-augmented drug development could help produce vaccines and treatments fast enough to halt the spread of deadly viruses before they mutate into global pandemics. Conventional methods for drug and vaccine development are wildly inefficient.

Studies have shown that AI can outperform human doctors at identifying breast cancer from ... [ ] mammograms. Here, a clinician interprets a mammogram in a hospital in France. There has been increased hand-wringing across the AI community in recent months about the limitations of deep learning. It was a dominant theme a few months ago at NeurIPS, the world's premier AI conference. In December, deep learning pioneer Yoshua Bengio and AI researcher Gary Marcus engaged in a high-profile televised debate about whether deep learning was the right path forward for AI.

The FDA has cleared what it describes as the first software that uses AI to guide family doctors, registered nurses and other clinicians in taking cardiac ultrasounds. Developed by Brisbane, California-based Caption Health, the software communicates instructions via prompts on a screen-based interface. The prompts allow non-experts to capture images and videos of diagnostic quality. "This is especially important because it demonstrates the potential for artificial intelligence and machine learning technologies to increase access to safe and effective cardiac diagnostics that can be life-saving for patients," Robert Ochs, a deputy director in the FDA's Center for Devices and Radiological Health, said in a statement. The software is called Caption Guidance and was cleared for use with a diagnostic ultrasound system developed by Teratech Corp., though the software has the potential to be used with other systems, according to the FDA. In granting clearance to the software, the agency said it looked at two independent studies.

Caption Health, a leading medical AI company, announced that the US Food and Drug Administration (FDA) authorized marketing of Caption Guidance, software that assists medical professionals in the acquisition of cardiac ultrasound images. Caption Guidance uses artificial intelligence to provide real-time guidance and diagnostic quality assessment of images, empowering healthcare providers--even those without prior ultrasound experience--with the ability to capture diagnostic quality images. Empowering more clinicians with ultrasound image acquisition capability will bring the benefits of ultrasound to more patients, help standardize the quality of care, and help institutions realize valuable cost and time savings. Caption Guidance was authorized via the De Novo pathway, a regulatory pathway reserved for novel technologies. The granting of this De Novo is groundbreaking, as Caption Guidance is the first medical software authorized by the FDA that provides real-time AI guidance for medical imaging acquisition.

CMS launches healthcare outcomes challenge. Expect more artificial intelligence (AI) in healthcare in 2020. We will see AI used primarily in diagnostics and auditing. In each of these areas, AI promises to impose drastic changes on society. As these changes reverberate through organizations, old work patterns will be disrupted.

Editor-at-Large based in London, United Kingdom, United Kingdom. Expertise in Internet, Music, Unemployment, Sexual health, Stocks & trading, see all» Education, General business news & info, Careers & workplace, Pharmaceuticals, Government, Environment & green living, Concerts, Small business, Celebrities, Books, Drinks, Video games, Science & space, Automotive, Sports, Technology, Movies, Board games, Charity & volunteer work, Jobs, Social media, Politics, Food, dining & restaurants, Travel, Pets, Health, Men's health, Food, recipes, Women's health, Entertainment Over the next decade, businesses will face maturing cybercrime and renewed nation-state cyberattacks. Both of these threats are key areas for which businesses need to be aware, as well as for governments to take action. Electric scooters are growing in popularity in many parts of the world. While the safety risks have been well-publicised, such as data posted by the U.S. CDC, the cybersecurity risks are not as well known – and yet these could be equally serious.

The evaluation of white matter lesion progression is an important biomarker in the follow-up of MS patients and plays a crucial role when deciding the course of treatment. Current automated lesion segmentation algorithms are susceptible to variability in image characteristics related to MRI scanner or protocol differences. We propose a model that improves the consistency of MS lesion segmentations in inter-scanner studies. First, we train a CNN base model to approximate the performance of icobrain, an FDA-approved clinically available lesion segmentation software. A discriminator model is then trained to predict if two lesion segmentations are based on scans acquired using the same scanner type or not, achieving a 78% accuracy in this task. Finally, the base model and the discriminator are trained adversarially on multi-scanner longitudinal data to improve the inter-scanner consistency of the base model. The performance of the models is evaluated on an unseen dataset containing manual delineations. The inter-scanner variability is evaluated on test-retest data, where the adversarial network produces improved results over the base model and the FDA-approved solution.